Duopoly Competition with Network Effects in Discrete Choice Models
Why this work is in the frame
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Bibliographic record
Abstract
It has been realized for a long time that network effects play an important role in how market participants compete with each other. Arguably, companies like Facebook and Google are able to gain immense market power by leveraging the network effects of their consumers, despite potential competitors. This paper investigates how the dynamics play out in duopoly competition. We find that when the network effects per unit of consumption are weak, the competitors can co-exist and gain even market shares. As network effects become stronger, it is unstable, and even impossible, for the firms to coexist, and one firm emerges victorious, taking the majority of the market. The study provides a theoretical analysis for commonly observed market phenomena. It may also have implications for antitrust legislation: Special policies need to be created to maintain a competitive market structure for products and services with strong network effects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it